Combining tree-boosting with Gaussian process and mixed effects models

A collection of boosting algorithms written in Rust 🦀

This repository contains a comprehensive guide and implementation of ensemble modeling techniques, specifically focusing on Boosting, Bagging, and Voting. Ensemble methods are powerful techniques in machine learning that combine the predictions from multiple models to improve overall performance and robustness.

Учебные материалы по курсам связанным с Машинным обучением, которые я читаю в УрФУ. Презентации, блокноты ipynb, ссылки

👩‍💻This repository contains implementations of various machine learning algorithms, along with example datasets and Jupyter Notebook files for demonstration.

"This repository contains implementations of Boosting method, popular techniques in Model Ensembles, aimed at improving predictive performance by combining multiple models. by using titanic database."

Our goal in this project was to develop statistical and machine learning models to replicate the functionality of the traditional Black-Scholes option pricing formula, specifically for valuing European call options.

Python files employed in my research

R markdown files employed in my research

This notebook explores fraud detection using various machine learning techniques.

Insanely fast Open Source Computer Vision library for ARM and x86 devices (Up to #50 times faster than OpenCV)

Project building ML & DL models to detect spam messages.

Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).

Analyze the data and come up with a predictive model to determine if a customer will leave the credit card services or not and the reason behind it

Профильное Задание VK

Regression, Classification, Clustering, Dimension-reduction, Anomaly detection

A collection of multiple projects involving tasks such as classification, time series forecasting , regression etc. on a number of datasets using different machine learning algorithms such as random forest, SVM, Naive Bayes, Ensemble, perceptron etc in addition to data cleaning and preparation.

The Steel Plates Faults dataset project utilizes machine learning to enhance quality control in steel manufacturing, aiming to develop models for efficient fault detection and classification. This initiative promises to improve productivity and reduce costs, ensuring the delivery of high-quality steel products to meet industry demands.

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.

Introduction to tree models with Python